Update readme

README.md

@@ -148,6 +148,26 @@ graphium-train --config-path [PATH] --config-name [CONFIG]
 ```
 Thanks to the modular nature of `hydra` you can reuse many of our config settings for your own experiments with Graphium.
 
+### Finetuning
+
+After pretraining a model and saving a model checkpoint, the model can be finetuned to a new task:
+```bash
+graphium-train +finetuning custom finetuning.pretrained_model=[model_identifier] constants.data_path=[path_to_data] constants.task=[name_of_task] constants.task_type=[cls OR reg]
+```
+
+The `[model_identifier]` serves to identify the pretrained model among those maintained in the `GRAPHIUM_PRETRAINED_MODELS_DICT` in `graphium/utils/spaces.py`, where the `[model_identifier]` maps to the location of the checkpoint of the pretrained model.
+
+The custom dataset to finetune from consists of two files `raw.csv` and `split.csv` that are provided in `[path_to_data]/[name_of_task]`. The `raw.csv` contains two columns, namely `smiles` with the smiles strings, and `target` with the corresponding targets. In `split.csv`, three columns `train`, `val`, `test` contain the indices of the rows in `raw.csv`. Examples can be found under `expts/data/finetuning_example-reg` (regression) and `expts/data/finetuning_example-cls` (binary classification).
+
+### Fingerprinting
+
+Alternatively, we can also obtain molecular embeddings (fingerprints) from a pretrained model:
+```bash
+graphium fps create custom pretrained.model=[model_identifier] pretrained.layers=[layer_identifiers] datamodelu.df_path=[path_to_data]
+```
+
+After specifiying the `[model_identifier]`, we need to provide a list of layers from that model where we want to read out embeddings via `[layer_identifiers]`. An example can be found in `expts/hydra-configs/fingerprinting/custom.yaml`. In addition, the location of the smiles to be embedded needs to be passed as `[path_to_data]`. The data can be passed as a csv file with a column `smiles`, similar to `expts/data/finetuning_example-reg/raw.csv`.
+
 ## License
 
 Under the Apache-2.0 license. See [LICENSE](LICENSE).

expts/hydra-configs/finetuning/custom.yaml

@@ -5,18 +5,16 @@ defaults:
 
 constants:
   benchmark: custom
-  task: task
-  # task_type: reg
-  # task_name: adme-fang-HPPB-reg
-  task_type: cls
-  task_name: CYP2D6_Veith
+  task: finetuning_example-cls # finetuning_example-cls OR finetuning_example-reg
+  task_type: cls  # cls OR reg
+  data_path: expts/data
   wandb:
-    name: finetune_${constants.task_name}
+    name: finetune_${constants.task}
     project: finetuning
     entity: valencelabs
     tags:
     - finetuning
-    - ${constants.task_name}
+    - ${constants.task}
     - ${finetuning.pretrained_model}
   seed: 42
   max_epochs: 20
@@ -34,10 +32,8 @@ datamodule:
     task_specific_args:
       finetune:
         df: null
-        # df_path: expts/data/finetuning_example-reg/raw.csv
-        # splits_path: expts/data/finetuning_example-reg/split.csv
-        df_path: expts/data/finetuning_example-cls/raw.csv
-        splits_path: expts/data/finetuning_example-cls/split.csv
+        df_path: ${constants.data_path}/${constants.task}/raw.csv
+        splits_path: ${constants.data_path}/${constants.task}/split.csv
         smiles_col: smiles
         label_cols: target
         task_level: graph
@@ -90,7 +86,7 @@ finetuning:
 
   # Optional finetuning head appended to model after finetuning_module
   # finetuning_head:
-  #   task: ${constants.task}
+  #   task: finetune
   #   previous_module: task_heads
   #   incoming_level: graph
   #   model_type: mlp

expts/hydra-configs/fingerprinting/custom.yaml

@@ -1,5 +1,6 @@
-pretrained_models:
-  dummy-pretrained-model:
+pretrained:
+  model: dummy-pretrained-model:
+  layers:
     - graph_output_nn-graph:0
     - task_heads-zinc:0
 

graphium/cli/fingerprint.py

@@ -31,7 +31,7 @@ def smiles_to_fps(cfg_name: str, overrides: List[str]) -> Dict[str, Any]:
         project="graphium-fingerprints",
     )
 
-    pretrained_models = cfg.get("pretrained_models")
+    pretrained_models = cfg.get("pretrained")
 
     # Allow alternative definition of `pretrained_models` with the single model specifier and desired layers
     if "layers" in pretrained_models.keys():